Automatic building detection and classification using elevator/escalator stairs modeling - smart cities

ABSTRACT

A system, a method and a computer program product are provided to determine population distribution of users associated with one or more buildings in a geographic region, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain mobility features associated with the one or more buildings in the geographic region. The processor may be configured to determine using a trained machine learning model, one or more transport modes for the one or more buildings, based on the mobility features. The processor may be further configured to determine, using the trained machine learning model, the population distribution of the users associated with the one or more buildings in the geographic region at a fixed epoch based on the determined one or more transport modes.

TECHNOLOGICAL FIELD

An example embodiment of the present invention generally relates todetermining population distribution of users for one or more buildingsin a geographic region, and more particularly relates to a system, amethod, and a computer program product for using a trained machinelearning model for determining the population distribution of users forthe one or more buildings in the geographic region.

BACKGROUND

Mobile industry has witnessed a rise in context aware mobile servicesthat collect sensor data from mobile phone users, analyze sensor datasets from the sensor data to identify attributes that may be furtherused to serve users (such as, the mobile phone users). Consequently,sensor data driven service innovation may be at rise, where mobileservice ideas or applications are generated based on patterns related toindividuals as well as social networks. Such applications may createcompelling user experiences based on the patterns extracted from thesensor data of mobile phones of users. Also, such patterns may be usedfor planning additional infrastructure for buildings, commute modes forthe buildings or in the adverse cases, for determining emergencyresponses for buildings and infrastructures. In certain scenarios, thereare applications (such as, but not limited to, navigation applications)where inaccurate determination of patterns may mar user experience andusability. In such scenarios, the applications may present a challengeand fail to satisfy accuracy demands of services (such as, locationbased services) and other planning related to the buildings andinfrastructures.

BRIEF SUMMARY

Accordingly, there is a need for determination of transport modes (suchas, but not limited to, elevators, escalators, and stairs) for one ormore buildings from sensor data obtained from one or more user equipment(UE). The determination of transport modes may further be used todetermine mobility patterns associated with user activities that areunique to each activity to be detected and determination of a buildingtype associated with one or more buildings in a geographic region.

Data regarding the user activities derived from the UE (such as, mobilephones) may facilitate data service innovations where mobile servicesare created based on the mobility patterns related to individuals andcommunities. The user activities may provide insights into a number ofservices, such as, but not limited to, street parking status, streetcongestion and city level planning.

The present disclosure provides a system, a method and a computerprogram product to determine a building type associated with one or morebuildings based on one or more transport modes associated with the oneor more buildings in a geographic region, using a trained machinelearning model, in accordance with various embodiments.

Embodiments of the disclosure provide a system for determining one ormore buildings in a geographic region. The system may comprise at leastone non-transitory memory configured to store computer executableinstructions and at least one processor (also referred to as aprocessor) configured to execute the computer executable instructions toobtain a plurality of mobility features associated with the one or morebuildings in the geographic region, determine, using a trained machinelearning model, one or more transport modes for the one or morebuildings, based on the plurality of mobility features; and determine,using the trained machine learning model, the population distribution ofthe users associated with the one or more buildings in the geographicregion at a fixed epoch based on the determined one or more transportmodes.

In accordance with an embodiment, the processor may be furtherconfigured to update map data for the one or more buildings, based onthe determined population distribution of users associated with the oneor more buildings.

In accordance with an embodiment, the processor may be furtherconfigured to transmit the updated map data to at least one subject,wherein the subject comprises at least one of a vehicle or a userequipment.

In accordance with an embodiment, the processor may be furtherconfigured to control one or more geo-location service applications on auser equipment based on the determined population distribution of theusers associated with the one or more buildings in the geographicregion.

In accordance with an embodiment, the one or more geo-location serviceapplications provide services comprising at least one of streetlightvolume, scheduling, trash pickup time, street congestion, trafficplanning, parking planning, transportation planning, public spaceplanning, or city level planning.

Embodiments of the disclosure provide a method to determine populationdistribution of users associated with one or more buildings in ageographic region. The method comprising obtaining a plurality ofmobility features associated with the one or more buildings in thegeographic region, determining, using a trained machine learning model,one or more transport modes for the one or more buildings, based on theplurality of mobility features; and determining, using the trainedmachine learning model, the population distribution of the usersassociated with the one or more buildings in the geographic region at afixed epoch based on the determined one or more transport modes.

Embodiments of the disclosure provide a computer program productcomprising at least one non-transitory computer-readable storage mediumhaving stored thereon computer-executable instructions which whenexecuted by a computer, cause the computer to carry out operations todetermine population distribution of users associated with one or morebuildings in a geographic region. The operations may comprise obtaininga plurality of mobility features associated with the one or morebuildings in the geographic region, determining, using a trained machinelearning model, one or more transport modes for the one or morebuildings, based on the plurality of mobility features; and determining,using the trained machine learning model, the population distribution ofthe users associated with the one or more buildings in the geographicregion at a fixed epoch based on the determined one or more transportmodes.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described example embodiments of the invention in generalterms, reference will now be made to the accompanying drawings, whichare not necessarily drawn to scale, and wherein:

FIG. 1 illustrates a schematic diagram of a network environment of asystem for determining one or more transport modes for one or morebuildings in a geographic region, in accordance with an exampleembodiment;

FIG. 2 illustrates a block diagram of the system, as illustrated in FIG.1, for determining one or more transport modes for one or more buildingsin a geographic region, in accordance with an example embodiment;

FIG. 3 illustrates a block diagram showing high level componentfunctionality of the system 102, for determining one or more transportmodes for one or more buildings in a geographic region, in accordancewith an example embodiment;

FIG. 4 illustrates a flowchart for implementation of an exemplary methodfor determining one or more transport modes for one or more buildings ina geographic region, in accordance with an embodiment;

FIG. 5 illustrates a schematic diagram of a system for determining oneor more buildings in a geographic region, in accordance with anembodiment;

FIG. 6 illustrates a schematic diagram of a system for determiningpopulation distribution of users for one or more buildings in ageographic region, in accordance with an embodiment;

FIGS. 7A and 7B, collectively illustrate schematic diagrams of a systemfor determining user profile of one or more users in one or morebuildings in a geographic region, in accordance with an embodiment;

FIGS. 8A and 8B, collectively illustrate schematic diagrams of a systemfor determining mobility pattern of one or more users for one or morebuildings in a geographic region, in accordance with an embodiment;

FIG. 9 illustrates a flowchart for implementation of an exemplary methodfor determining one or more buildings in a geographic region, inaccordance with an example embodiment;

FIG. 10 illustrates a flowchart for implementation of an exemplarymethod for determining population distribution of users for one or morebuildings in a geographic region, in accordance with an exampleembodiment;

FIG. 11 illustrates a flowchart for implementation of an exemplarymethod for determining user profile of one or more users in one or morebuildings in a geographic region, in accordance with an embodiment; and

FIG. 12 illustrates a flowchart for implementation of an exemplarymethod for determining mobility pattern of one or more users for one ormore buildings in a geographic region, in accordance with an embodiment.

DETAILED DESCRIPTION

A system, a method, and a computer program product are provided hereinin accordance with an example embodiment for determining one or moretransport modes for one or more buildings in a geographic region. Insome example embodiments, a method, a system, and a computer programproduct provided herein may also be used for classifying one or morebuildings into different types of buildings. In some exampleembodiments, a method, a system, and a computer program product providedherein may also be used for determining population distribution of usersfor one or more buildings in a geographic region. In some exampleembodiments, a method, a system, and a computer program product providedherein may also be used for determining user profile of one or moreusers in one or more buildings. In further example embodiments, amethod, a system, and a computer program product provided herein mayalso be used for determining mobility pattern of one or more users forone or more buildings. The systems, the methods, and the computerprogram products disclosed herein provide utilization of sensors on themobile devices (such as, mobile phones, tablets or wearable devices) ofthe users.

From the sensors, mobility patterns may be determined that are unique toeach activity. User mobility data from the mobile phones may facilitatedata service innovations where mobile services are created based onpatterns related to individuals as well as communities. Therefore,embodiments of the present disclosure provide user experiences based onmobility patterns extracted from the sensors on the mobile devices ofthe users.

In accordance with an embodiment, the system, the method, and thecomputer program product disclosed herein may further provide anotification message indicating a classification of a building at acertain instance of time. For example, the notification message mayinform a vehicle or user equipment about availability of accurate andup-to-date building data for a certain geographic region. Alternatively,the available up-to-date building data may be pushed as an update to thevehicle or user equipment. In this way, a beneficiary of the data (suchas, the vehicle) may be provided with highly accurate commuteinformation inside buildings in a geographic area based on theup-to-date data. These and other technical improvements of the presentdisclosure will become evident from the description provided herein.

In accordance with an embodiment, the system, the method, and thecomputer program product disclosed herein may further be used with anavigation application which may be integrated with maps (such as streetmaps, indoor maps or models).

FIG. 1 illustrates a schematic diagram of a network environment 100 of asystem in accordance with an example embodiment. The system 102 may becommunicatively coupled with, a user equipment (UE) 104 an OEM cloud106, a mapping platform 108, via a network 110. The mapping platform 108may further include a server 108A and a database 108B. The userequipment includes an application 104A, a user interface 104B, and asensor unit 104C. Further, the server 108A and the database 108B may becommunicatively coupled to each other.

The system 102 may comprise suitable logic, circuitry, interfaces andcode that may be configured to process the sensor data obtained from theUE 104 for activity inference, such as determination of one or moretransport modes for one or more buildings in a geographic region. Thesystem 102 may be communicatively coupled to the UE 104, the OEM cloud106, and the mapping platform 108 directly via the network 110.Additionally, or alternately, in some example embodiments, the system102 may be communicatively coupled to the UE 104 via the OEM cloud 106which in turn may be accessible to the system 102 via the network 110.

All the components in the network environment 100 may be coupleddirectly or indirectly to the network 110. The components described inthe network environment 100 may be further broken down into more thanone component and/or combined together in any suitable arrangement.Further, one or more components may be rearranged, changed, added,and/or removed. Furthermore, fewer or additional components may be incommunication with the system 102, within the scope of this disclosure.

The system 102 may be embodied in one or more of several ways as per therequired implementation. For example, the system 102 may be embodied asa cloud based service or a cloud based platform. As such, the system 102may be configured to operate outside the UE 104. However, in someexample embodiments, the system 102 may be embodied within the UE 104.In each of such embodiments, the system 102 may be communicativelycoupled to the components shown in FIG. 1 to carry out the desiredoperations and wherever required modifications may be possible withinthe scope of the present disclosure.

The UE 104 may be any user accessible device, such as, a mobile phone, asmartphone, a portable computer, a wearable device (such as a fitnessband) and the like that is portable in itself or as a part of anotherportable/mobile object, such as, a vehicle. The UE 104 may comprise aprocessor, a memory and a network interface. The processor, the memoryand the network interface may be communicatively coupled to each other.In some example embodiments, the UE 104 may be associated, coupled, orotherwise integrated with a vehicle of the user, such as an advanceddriver assistance system (ADAS), a personal navigation device (PND), aportable navigation device, an infotainment system and/or other devicethat may be configured to provide route guidance and navigation relatedfunctions to the user. In such example embodiments, the UE 104 maycomprise processing means such as a central processing unit (CPU),storage means such as on-board read only memory (ROM) and random accessmemory (RAM), acoustic sensors such as a microphone array, positionsensors such as a GPS sensor, gyroscope, a LIDAR sensor, a proximitysensor, motion sensors such as accelerometer, a display enabled userinterface such as a touch screen display, and other components as may berequired for specific functionalities of the UE 104. Additional,different, or fewer components may be provided. For example, the UE 104may be configured to execute and run mobile applications such as amessaging application, a browser application, a navigation application,and the like. In accordance with an embodiment, the UE 104 may bedirectly coupled to the system 102 via the network 110. For example, theUE 104 may be a dedicated vehicle (or a part thereof) for gathering datafor development of the map data in the database 108B. In some exampleembodiments, the UE 104 may be coupled to the system 102 via the OEMcloud 106 and the network 110. For example, the UE 104 may be a consumermobile phone (or a part thereof) and may be a beneficiary of theservices provided by the system 102. In some example embodiments, the UE104 may serve the dual purpose of a data gatherer and a beneficiarydevice. The UE 104 may be configured to provide sensor data to thesystem 102. In accordance with an embodiment, the UE may process thesensor data locally for the activity inference, such as determination ofone or more transport modes for one or more buildings in a geographicregion. Further, in accordance with an embodiment, the UE 104 may beconfigured to perform processing related to classification of one ormore transport modes for one or more buildings locally.

The UE 104 may include the application 104A with the user interface 104Bto access one or more applications. The application 104B may correspondto, but not limited to, map related service application, navigationrelated service application and location based service application. Inother words, the UE 104 may include the application 104A with the userinterface 104B.

The sensor unit 104C may be embodied within the UE 104. The sensor unit104C comprising one or more sensors may capture sensor data, in acertain geographic location. In accordance with an embodiment, thesensor unit 104C may be built-in, or embedded into, or within interiorof the UE 104. The one or more sensors (or sensors) of the sensor unit104C may be configured to provide the sensor data comprising locationdata associated with a location of a user. In accordance with anembodiment, the sensor unit 104C may be configured to transmit thesensor data to an Original Equipment Manufacturer (OEM) cloud. Examplesof the sensors in the sensor unit 104C may include, but not limited to,a microphone, a camera, an acceleration sensor, a gyroscopic sensor, aLIDAR sensor, a proximity sensor, and a motion sensor.

The sensor data may refer to sensor data collected from a sensor unit104C in the UE 104. In accordance with an embodiment, the sensor datamay be collected from a large number of mobile phones. In accordancewith an embodiment, the sensor data may refer to the point cloud data.The point cloud data may be a collection of data points defined by agiven coordinates system. In a 3D coordinates system, for instance, thepoint cloud data may define the shape of some real or created physicalobjects. The point cloud data may be used to create 3D meshes and othermodels used in 3D modelling for various fields. In a 3D Cartesiancoordinates system, a point is identified by three coordinates that,taken together, correlate to a precise point in space relative to apoint of origin. The LIDAR point cloud data may include pointmeasurements from real-world objects or photos for a point cloud datathat may then be translated to a 3D mesh or NURBS or CAD model. Inaccordance with an embodiment, the sensor data may be converted to unitsand ranges compatible with the system 102, to accurately receive thesensor data at the system 102. Additionally, or alternately, the sensordata of a user equipment may correspond to movement data associated witha user of the user equipment. Without limitations, this may includemotion data, position data, orientation data with respect to a referenceand the like.

The mapping platform 108 may comprise suitable logic, circuitry,interfaces and code that may be configured to store map data associatedwith layout of one or more buildings. The layout may include position ofthe one or more transport modes for one or more buildings in ageographic region. The server 108A of the mapping platform 108 maycomprise processing means and communication means. For example, theprocessing means may comprise one or more processors configured toprocess requests received from the system 102 and/or the UE 104. Theprocessing means may fetch map data from the database 108B and transmitthe same to the system 102 and/or the UE 104 in a suitable format. Inone or more example embodiments, the mapping platform 108 mayperiodically communicate with the UE 104 via the processing means toupdate a local cache of the map data stored on the UE 104. Accordingly,in some example embodiments, map data may also be stored on the UE 104and may be updated based on periodic communication with the mappingplatform 108.

The database 108B of the mapping platform 108 may store map data of oneor more geographic regions that may correspond to a city, a province, acountry or of the entire world. The database 108B may store point clouddata collected from the UE 104. The database 108B may store data suchas, but not limited to, node data, road segment data, link data, pointof interest (POI) data, link identification information, and headingvalue records. The database 108B may also store cartographic data,routing data, and/or maneuvering data. According to some exampleembodiments, the road segment data records may be links or segmentsrepresenting roads, streets, or paths, as may be used in calculating aroute or recorded route information for determination of one or morepersonalized routes. The node data may be end points corresponding tothe respective links or segments of road segment data. The road linkdata and the node data may represent a road network, such as used byvehicles, cars, trucks, buses, motorcycles, and/or other entities foridentifying location of building.

Optionally, the database 108B may contain path segment and node datarecords, such as shape points or other data that may representpedestrian paths, links or areas in addition to or instead of thevehicle road record data. The road/link segments and nodes can beassociated with attributes, such as geographic coordinates, streetnames, address ranges, speed limits, turn restrictions at intersections,and other navigation related attributes, as well as POIs, such asfueling stations, hotels, restaurants, museums, stadiums, offices, autorepair shops, buildings, stores, parks, etc. The database 108B may alsostore data about the POIs and their respective locations in the POIrecords. The database 108B may additionally store data about places,such as cities, towns, or other communities, and other geographicfeatures such as bodies of water, and mountain ranges. Such place orfeature data can be part of the POI data or can be associated with POIsor POI data records (such as a data point used for displaying orrepresenting a position of a city). In addition, the database 108B mayinclude event data (e.g., traffic incidents, construction activities,scheduled events, unscheduled events, accidents, diversions etc.)associated with the POI data records or other records of the database108B. Optionally or additionally, the database 108B may store 3Dbuilding maps data (3D map model of objects) of structures surroundingroads and streets.

The database 108B may be a master map database stored in a format thatfacilitates updating, maintenance, and development. For example, themaster map database or data in the master map database may be in anOracle spatial format or other spatial format, such as for developmentor production purposes. The Oracle spatial format ordevelopment/production database may be compiled into a delivery format,such as a geographic data files (GDF) format. The data in the productionand/or delivery formats may be compiled or further compiled to formgeographic database products or databases, which may be used in end usernavigation devices or systems.

For example, geographic data may be compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by the UE 104. The navigation-related functions maycorrespond to vehicle navigation, pedestrian navigation, or other typesof navigation. The compilation to produce the end user databases may beperformed by a party or entity separate from the map developer. Forexample, a customer of the map developer, such as a navigation devicedeveloper or other end user device developer, may perform compilation ona received map database in a delivery format to produce one or morecompiled navigation databases.

As mentioned above, the database 108B may be a master geographicdatabase, but in alternate embodiments, the database 108B may beembodied as a client-side map database and may represent a compilednavigation database that may be used in or with end user devices (suchas the UE 104) to provide navigation and/or map-related functions. Insuch a case, the database 108B may be downloaded or stored on the enduser devices (such as the UE 104).

The network 110 may comprise suitable logic, circuitry, and interfacesthat may be configured to provide a plurality of network ports and aplurality of communication channels for transmission and reception ofdata, such as the sensor data, map data from the database 108B, etc.Each network port may correspond to a virtual address (or a physicalmachine address) for transmission and reception of the communicationdata. For example, the virtual address may be an Internet ProtocolVersion 4 (IPv4) (or an IPv6 address) and the physical address may be aMedia Access Control (MAC) address. The network 110 may be associatedwith an application layer for implementation of communication protocolsbased on one or more communication requests from at least one of the oneor more communication devices. The communication data may be transmittedor received, via the communication protocols. Examples of such wired andwireless communication protocols may include, but are not limited to,Transmission Control Protocol and Internet Protocol (TCP/IP), UserDatagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), FileTransfer Protocol (FTP), ZigBee, EDGE, infrared (IR), IEEE 802.11,802.16, cellular communication protocols, and/or Bluetooth (BT)communication protocols.

Examples of the network 110 may include, but is not limited to awireless channel, a wired channel, a combination of wireless and wiredchannel thereof. The wireless or wired channel may be associated with anetwork standard which may be defined by one of a Local Area Network(LAN), a Personal Area Network (PAN), a Wireless Local Area Network(WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN),Wireless Wide Area Network (WWAN), a Long Term Evolution (LTE) networks(for e.g. LTE-Advanced Pro), 5G New Radio networks, ITU-IMT 2020networks, a plain old telephone service (POTS), and a Metropolitan AreaNetwork (MAN). Additionally, the wired channel may be selected on thebasis of bandwidth criteria. For example, an optical fiber channel maybe used for a high bandwidth communication. Further, a coaxialcable-based or Ethernet-based communication channel may be used formoderate bandwidth communication.

FIG. 2 illustrates a block diagram 200 of the system 102, exemplarilyillustrated in FIG. 1, for determining one or more transport modes forone or more buildings in a geographic region, in accordance with anexample embodiment. FIG. 2 is described in conjunction with elementsfrom FIG. 1.

As shown in FIG. 2, the system 102 may comprise a processing means suchas a processor 202, storage means such as a memory 204, a communicationmeans, such as a network interface 206, an input/output (I/O) interface208, and a machine learning model 210. The processor 202 may retrievecomputer executable instructions that may be stored in the memory 204for execution of the computer executable instructions. The system 102may connect to the UE 104 via the I/O interface 208. The processor 202may be communicatively coupled to the memory 204, the network interface206, the I/O interface 208, and the machine learning model 210.

The processor 202 may comprise suitable logic, circuitry, and interfacesthat may be configured to execute instructions stored in the memory 204.The processor 202 may obtain sensor data associated with the one or morebuildings for time duration. The sensor data may be captured by one ormore UE, such as the UE 104. The processor 202 may be configured todetermine mobility features associated with the one or more buildings,based on the sensor data. The processor 202 may be further configured todetermine, using a trained machine learning model, the one or moretransport modes for the one or more buildings, based on the mobilityfeatures.

Examples of the processor 202 may be an Application-Specific IntegratedCircuit (ASIC) processor, a Complex Instruction Set Computing (CISC)processor, a central processing unit (CPU), an Explicitly ParallelInstruction Computing (EPIC) processor, a Very Long Instruction Word(VLIW) processor, and/or other processors or circuits. The processor 202may implement a number of processor technologies known in the art suchas a machine learning model, a deep learning model, such as a recurrentneural network (RNN), a convolutional neural network (CNN), and afeed-forward neural network, or a Bayesian model. As such, in someembodiments, the processor 202 may include one or more processing coresconfigured to perform independently. A multi-core processor may enablemultiprocessing within a single physical package.

Additionally, or alternatively, the processor 202 may include one ormore processors configured in tandem via the bus to enable independentexecution of instructions, pipelining and/or multithreading.Additionally, or alternatively, the processor 202 may include one orprocessors capable of processing large volumes of workloads andoperations to provide support for big data analysis. However, in somecases, the processor 202 may be a processor specific device (forexample, a mobile terminal or a fixed computing device) configured toemploy an embodiment of the disclosure by further configuration of theprocessor 202 by instructions for performing the algorithms and/oroperations described herein.

In some embodiments, the processor 202 may be configured to provideInternet-of-Things (IoT) related capabilities to users of the UE 104disclosed herein. The IoT related capabilities may in turn be used toprovide smart city solutions by providing real time parking updates, bigdata analysis, and sensor based data collection for providing navigationand parking recommendation services. The environment may be accessedusing the I/O interface 208 of the system 102 disclosed herein.

The memory 204 may comprise suitable logic, circuitry, and interfacesthat may be configured to store a machine code and/or instructionsexecutable by the processor 202. The memory 204 may be configured tostore information including processor instructions for training themachine learning model. The memory 204 may be used by the processor 202to store temporary values during execution of processor instructions.The memory 204 may be configured to store different types of data, suchas, but not limited to, sensor data from the UE 104. Examples ofimplementation of the memory 204 may include, but are not limited to,Random Access Memory (RAM), Read Only Memory (ROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD),a Solid-State Drive (SSD), a CPU cache, and/or a Secure Digital (SD)card.

The network interface 206 may comprise suitable logic, circuitry, andinterfaces that may be configured to communicate with the components ofthe system 102 and other systems and devices in the network environment100, via the network 110. The network interface 206 may communicate withthe UE 104, via the network 110 under the control of the processor 202.In one embodiment, the network interface 206 may be configured tocommunicate with the sensor unit 104C disclosed in the detaileddescription of FIG. 1. In an alternative embodiment, the networkinterface 206 may be configured to receive the sensor data from the OEMcloud 106 over the network 110 as described in FIG. 1. In some exampleembodiments, the network interface 206 may be configured to receivelocation information of a user associated with a UE (such as, the UE104), via the network 110. In accordance with an embodiment, acontroller of the UE 104 may receive the sensor data from a positioningsystem (for example: a GPS based positioning system) of the UE 104. Thenetwork interface 206 may be implemented by use of known technologies tosupport wired or wireless communication of the system 102 with thenetwork 110. Components of the network interface 206 may include, butare not limited to, an antenna, a radio frequency (RF) transceiver, oneor more amplifiers, a tuner, one or more oscillators, a digital signalprocessor, a coder-decoder (CODEC) chipset, a subscriber identity module(SIM) card, and/or a local buffer circuit.

The I/O interface 208 may comprise suitable logic, circuitry, andinterfaces that may be configured to operate as an I/O channel/interfacebetween the UE 104 and different operational components of the system102 or other devices in the network environment 100. The I/O interface208 may facilitate an I/O device (for example, an I/O console) toreceive an input (e.g., sensor data from the UE 104 for a time duration)and present an output to one or more UE (such as, the UE 104) based onthe received input. In accordance with an embodiment, the I/O interface208 may obtain the sensor data from the OEM cloud 106 to store in thememory 202. The I/O interface 208 may include various input and outputports to connect various I/O devices that may communicate with differentoperational components of the system 102. In accordance with anembodiment, the I/O interface 208 may be configured to output the one ormore transport modes to a user device, such as, the UE 104 of FIG. 1.

In example embodiments, the I/O interface 208 may be configured toprovide the data associated with determined one or more transport modesto the database 108A to update the map of a certain geographic region.In accordance with an embodiment, a user requesting information in ageographic region may be updated about the detection and classificationof a building. Examples of the input devices may include, but is notlimited to, a touch screen, a keyboard, a mouse, a joystick, amicrophone, and an image-capture device. Examples of the output devicesmay include, but is not limited to, a display, a speaker, a hapticoutput device, or other sensory output devices.

In accordance with an embodiment, the processor 202 may train themachine learning model 210 to determine the one or more transport modesfor one or more buildings in a geographic region. In accordance with anembodiment, the machine learning model 210 may be trained offline toobtain a classifier model to determine the one or more transport modesfor one or more buildings in a geographic region as a function ofmobility features that represent motion dynamics or stationarityassociated with one or more transport modes and users commuting on theone or more transport modes. For the training of the machine learningmodel 210, different feature selection techniques and classificationtechniques may be used. The system 102 may be configured to obtain thetrained machine learning model 210 and determine mobility features fromthe sensor data obtained from the one or more UE, such as the UE 104 fordetermination of the one or more transport modes for the one or morebuildings in the geographic region.

Datasets comprising the sensor data may be used for building the machinelearning model 210 with all transport modes to be determined. Forbuilding the machine learning model 210, the sensor data may be obtainedfor fixed time duration, and a reference transport mode may be assignedin the training data (such as, the sensor data) to learn from. Further,the mobility features that represent motion dynamics or stationarity maybe determined, stored and fed to the machine learning model 210 buildingtechnique. Further, for building the machine learning model 210, thesensor data may be fed to the model building technique to run it tobuild and obtain the machine learning model 210. The transport mode maybe a target output used to build the machine learning model 210, and themobility features that represent motion dynamics or stationarityconstitute as input to the machine learning model 210 corresponding tothe target output. In accordance with an embodiment, the machinelearning model building technique may correspond to a classificationtechnique, such as, but not limited to, decision trees and randomforest.

In accordance with an embodiment, various data sources may provide thesensor data as an input to the machine learning model 210. In accordancewith an embodiment, mobility features may be provided as an input to themachine learning model 210. Examples of the machine learning model 210may include, but not limited to, Decision Tree (DT), Random Forest, andAda Boost. In accordance with an embodiment, the memory 204 may includeprocessing instructions for training of the machine learning model 210with data set that may be real-time (or near real time) data orhistorical data. In accordance with an embodiment, the data may beobtained from one or more service providers.

FIG. 3 illustrates a block diagram 300 showing a process flow of highlevel component functionality of the system 102, for determining one ormore transport modes for one or more buildings in a geographic region,in accordance with an example embodiment. FIG. 3 is explained inconjunction with FIG. 1 and FIG. 2. There is shown the process flow ofthe system 102, viz., sensor data 302, pre-processing 304, featureextraction 306, feature vectorization 308, local embedding 310,classification 312 and output data 314 from the system 102.

The processor 202 of the system 102 may be configured to obtain thesensor data 302 associated with the one or more buildings in ageographic region for time duration. The processor 202 of the system 102may be configured to obtain the sensor data 302 from the sensor unit(such as, the sensor unit 104C) of one or more UE (such as, the UE 104).In accordance with an embodiment, the sensor unit 104C of the UE 104 mayinclude, but not limited to, a gyroscopic sensor, an accelerometer, amagnetometer, and a rotation sensor. The sensor data obtained from thesensor unit 104C may include, but not limited to, motion, position anddirection of the one or more transport modes (such as, but not limitedto, stairs, elevator, and escalator) associated with the one or morebuildings in the geographic region.

In an example embodiment, acceleration data (or sensor data) may beextracted from the accelerometer and orientation data (or sensor data)may be extracted from the gyroscopic sensor. In accordance with anembodiment, the accelerometer of the sensor unit 104C may be configuredto measure acceleration in all three axes (i.e. x, y, and z). From theUE 104, the acceleration with gravity and without gravity may beaccessed by the system 102 which may facilitate computation ofstatistical features for mobility features that are unique for aparticular activity. Square, and sum of area encompassed by magnitude ofthe readings on all the three axes (i.e. x, y, and z) may facilitatebuilding of a classification model (such as, the machine learning model210) to detect or infer current context of a user probabilistically. Inaccordance with an embodiment, GPS sensor may provide locationpositioning data to the system 102. The location positioning data mayprovide latitude, longitude, speed, acceleration, and heading changeinformation to the system 102.

Examples of the accelerometer may include, but not limited to,piezoelectric, charge mode piezoelectric, variable capacitance,microelectromechanical systems (MEMS). Further, the gyroscopic sensormay measure rotational motion or angular velocity. Examples of thegyroscopic sensor may include, but not limited to gyroscope is amicroelectromechanical system (MEMS) or Coriolis vibratory gyroscope. Insome instances, the gyroscopic sensor may be combined with theaccelerometer to measure the sensor data.

The sensor data may be extracted around the different coordinate axes ofthe one or more buildings in the geographic region. In accordance withan embodiment, the sensor data obtained from sensor units of one or moreUE (such as the UE 104) may correspond to raw sensor data that may beunprocessed or minimally processed data. The one or more buildings mayinclude, but not limited to, a residential building, a hospital, anoffice building, a mall, and a metro station.

In accordance with an embodiment, the UE 104 may transmit the sensordata immediately to the system 102 after capturing the sensor data. Inaccordance with an alternate embodiment, the UE 104 may transmit thesensor data to the system 102 in batches. In such a case, the system 102may access one or more UE periodically (similar to the UE 104) forobtaining the sensor data.

The system 102 may use the sensor data obtained from a large number ofUE to accurately determine a building type of the one or more buildingsin the geographic region. The sensor data obtained from sensors of theUE, such as smartphones have an advantage of being installed on a largeclass of smart phones, having a low-energy footprint.

At 304, processing of the sensor data may be performed. In accordancewith an embodiment, the sensor data obtained from sensor units of one ormore UE (such as the UE 104) may correspond to raw sensor data that maybe unprocessed or minimally processed data. Therefore, the system 102may be configured to process the sensor data. The processing of thesensor data may include, but not limited to, fusing and de-noising ofthe sensor data. In some example embodiments, the system 102 may receiveprocessed sensor data from one or more sources and in such scenarios,step 304 may be skipped.

At 306, the system 102 may be configured to extract a plurality ofmobility features (hereinafter referred as mobility features) from theprocessed sensor data. The system 102 may be configured to extract themobility features so that traveling patterns associated with the one ormore transport modes may be determined in sensor traces from the sensordata obtained from the one or more UE (such as, the UE 104). The one ormore transport modes may correspond to modes of commute inside the oneor more buildings and may include, but not limited to, stairs, elevatorsand escalators. Different mobility features may be used to detect a sameclass (of transport modes) accurately. The feature extraction ofmobility features is a step needed to extract properties of the sensordata which may help in discrimination of different transport modes fromthe one or more transport modes. Further, the feature extraction may aidin representing each sample by a feature vector which is discussed instep 308.

The mobility features that may be useful for classification of the oneor more transport modes may be created for training the machine learningmodel 210 during training or learning phase of the machine learningmodel 210. The machine learning model 210 may include base-level models,such as, but not limited to, Decision Trees and more advanced complexmeta-level classification models, such as Voting and Stacking. Themobility features may correspond to one or more attributes of the one ormore transport modes in the one or more buildings. The one or moreattributes may comprise one or more of vertical mobility signals andtransport mode usage at predefined epoch. For example, while a user usesthe one or more transport modes, such as, stairs, elevators andescalators, the sensor data obtained from UE of the user (such as, theUE 104) may have strong vertical mobility signals as compared to walkingor driving in a vehicle. The transport mode usage may correspond tonumber of times the one or more transport modes are used. For example,fraction of daily usage of the one or more transport modes by the usermay be miniscule as compared to walking, driving, and standing-still.

The one or more attributes may further comprise mean of acceleration,standard deviation of acceleration, yx ratio of acceleration, high peakand low peak of acceleration, correlation of acceleration, and SignalMagnitude Area (SMA) of acceleration.

The mean of acceleration may correspond to mean acceleration in x, y,and z axis that may vary across the one or more transport modes. Forexample, for elevators, vertical accelerometer reading should beprevalent since elevators travel vertically. Apart from the mean in theindividual axis (i.e. x, y, and z), the mean across the three axes mayalso be used as a mobility feature. The mean of acceleration maycorrespond to statistical feature of the mobility features. The threeaxes may correspond to axes in a 3D space.

The standard deviation of acceleration may correspond to the standarddeviation in the x, y, and z axis that may be used to capture the factthat the range of possible acceleration values differ between one ormore transport mode activities. The standard deviation of accelerationmay correspond to statistical feature of the mobility features.

The yx ratio of acceleration may correspond to acceleration in the yaxis that may help to identify the one or more transport mode activitiesthat have vertical movement components such as riding elevators,escalators or climbing/descending stairs. However, the yx ratio may bedifferent for all transport modes. For example, even though elevatorshave y axis acceleration, they may have limited x axis acceleration.Likewise, escalators have y axis acceleration and also x axisacceleration. The yx ratio of acceleration may correspond to statisticalfeature of the mobility features.

The high peak and low peaks of acceleration may correspond to high andlow peak acceleration in the x, y, and z axes from a set of the sensordata from an accelerometer. Unlike the mean acceleration, the high andlow peak acceleration for a set of the sensor data may detect suddenbrisk movements. More specifically, stair climbing activity may producehigher y axis acceleration peak values than elevator or escalator.

The correlation of acceleration may correspond to correlation measuresfor dependency between two variables. In accordance with an embodiment,the correlation may be calculated between each pair of accelerometeraxis (i.e. xy, xz, and yz) as a ratio of covariance and the product ofthe standard deviations. Climbing stairs may be translated in multipledimensions. In accordance with an example embodiment, the corr (y, z)which corresponds to correlation between the y and z axes, for stairsand walk, the corr (y, z) may have low values since the standarddeviation for acceleration in the y and z axes is high. On the otherhand, elevators have high corr (y, z) since it has smaller deviationsand more profound vertical movements.

The SMA of acceleration may be used on user activity recognition todistinguish between resting states and other user activities. For theSMA, the sum of the area encompassed by all three signals may be used tocompute the energy expenditure. Since the SMA may be equal to the sum ofthe area encompassed by the three axes, the one or more transport modeactivities, such as walking, may have higher SMA than being stationary.

The system 102 may be configured to determine the transport mode that isbeing used from the one or more transport modes after obtaining thesensor data because the system 102 may be configured to use the sensordata to infer the presence of a stair, an elevator or an escalator at alocation, or to automatically determine whether users use the elevators,the escalators, or the stairs. For example, for the elevators, thedominant vertical movement makes it easily distinguishable from climbingthe stairs and riding the escalator. Further, for stairs, the peaks fromthe accelerometer (from the sensor unit 104C), among other mobilityfeatures may make it distinguishable. Furthermore, the elevator may beunique in the sense that the vertical movements of the elevator may bepredominant. Also, the vibrations of the elevator as it stops andproceeds at each floor may be distinctive. In the case of accelerometersignals of the escalator and the stairs, such vertical movements and thevibrations may not be present.

In addition to determining accuracy of determining the one or moretransport modes by the system 102, it may further be imperative to knowwhich sensor(s) (such as, gyroscope or accelerometer) from the sensorunit (such as, the sensor unit 104C) may be more important fordetermination of the one or more transport modes. Therefore, todetermine the most effective sensor used, feature selection schemes maybe used to rank the mobility features used for determination of the oneor more transport modes. Feature selection (from the mobility features)may be a data-mining concept which chooses the subset of input features(from the mobility features) by eliminating classification features (orthe mobility features) that are less predictive. The system 102 may beconfigured to calculate confidence scores for each of the mobilityfeatures. The confidence scores may correspond to a rank of each of themobility features. An order of the rank may start with a top rankedmobility feature that corresponds to a most informative mobilityfeature. In accordance with an embodiment, for feature selection,Information Gain feature selection algorithm may be used. In accordancewith an example embodiment, the Information Gain ranking algorithmprovides evidence that the accelerometer is more effective than thegyroscope for the determination of the one or more transport modes,since the accelerometer classification features (or mobility features)are more effective than the gyroscope classification features.

In accordance with an embodiment, for inference to determine the one ormore transportation mode, the same mobility features utilized/created intraining the machine learning model 210 may be extracted by the system102. Subsequently, given these mobility features, the system 102 withalready trained machine learning model 210 may be configured to predictthe one or more transport modes of a user probabilistically.

For training the machine learning model 210, traces on different modesof transportation (such as, walking, stationary, stairs, elevator, andescalator) may be collected by the system 102. One limitation may be thesize of dataset considered. Realistically, more sensor data (such as,from the accelerometer) may be obtained in an uncontrolled manner from aplurality of users to effectively study and validate the machinelearning model 210. In accordance with an embodiment, the datasetconsidered may facilitate classification of the one or more transportmodes; however, with more sensor data that includes noise, the accuracyof classification may be reduced which may affect the determination ofthe one or more transport modes. Therefore, feature vectorization may beused to reduce the size of the dataset. Further, the system 102 may beconfigured to use a selective learning machine learning model combinedwith local learning techniques.

At 308, the system 102 may be configured to perform featurevectorization on extracted mobility features to determine feature vectorusing the machine learning model 210. In machine learning, the featurevector may correspond to an n-dimensional vector of numerical features(mobility features with confidence scores) that represent some object.In other words, a group of features or values representing the samplemay correspond to the feature vector. The numerical representation ofobjects or samples may facilitate processing and statistical analysis.

In accordance with an embodiment, training data of the machine learningmodel 210 may comprise input-target vector pairs, which is only a finitesample describing input space. The machine learning model 210 may beconfigured to find a good approximation to a function that relates inputvectors to corresponding target vectors, not only for training space,but for entire input space. That is, the trained machine learning model210 may generalize well which may be achieved by iteratively adjustingweights.

Feature selection criteria may be generated during the training phase ofthe machine learning model 210. In feature selection, the most suitablesubset of mobility features may be selected by the system 102, using themachine learning model 210. The machine learning model 210 maycorrespond to a selective learning machine learning model. The selectivelearning machine learning model (or the machine learning model 210) mayuse an active learning strategy that effectively “prunes” originaltraining set (or the dataset) during training. In accordance with anembodiment, a Neural Network of the machine learning model may use itscurrent learned knowledge to select at each selection interval a subsetof informative patterns from candidate dataset. Only the mostinformative patterns, which are those patterns closest to decisionboundaries, are selected for training to show a significant reduction inthe training dataset size. Selected patterns are not removed from thecandidate set. At each selection interval, all candidate patterns have achance to be selected. For example, decision tree algorithm may includea windowing strategy to perform pattern selection in cases of extremelylarge datasets.

At 310, the system 102 may be configured to perform local embedding onthe feature vectors, using the machine learning model 210. A localembedding may correspond to a relatively low-dimensional space intowhich high-dimensional vectors may be translated. The local embeddingmakes it easier to do machine learning on large inputs like the sensordata. An embedding can be learned and reused across models (such as, themachine learning model 210). The local embedding may be useful becausethe local embedding may reduce dimensionality of categorical variables(of the mobility features) and meaningfully represent categories in atransformed space by using feature transformation. The featuretransformation may obtain a mathematical transformation of the featurevectors to create a new feature vector which is better and morerepresentable of the transport modes. The feature transformation may begenerated during the training phase of the machine learning model 210.

At 312, the system 102 may be configured to perform classification onthe transformed feature vectors. Classification may correspond to aprocess of determining the transport mode during a certain period giventhe feature values. In classification, the feature vector is fed into atrained machine learning model 210 (or previously generated machinelearning model) whose output is one of the classes. The classes maycorrespond to a list of transport modes. The generation of the machinelearning model 210 may use any machine learning technique or anyclassification technique.

In accordance with an embodiment, the system 102 may use classificationwith a rejection option that consists of simultaneously learning twofunctions, that is, a classifier along with a rejection function. Therejection function may be confidence-based rejection based on theconfidence scores. In accordance with an embodiment, different datamining algorithms may be used for classification, such as, but notlimited to, base-level and meta-level classifiers.

The selective learning machine learning model may help to classify theone or more transport modes where a pattern may be identified as beingclassified correctly or not. When the machine learning model 210 may bealready sure of classification of a pattern, there is no need tore-learn that pattern. However, during the learning process, the machinelearning model 210 may become uncertain about a previously correctclassification, in which case the corresponding pattern should bebrought back into the training subset. A selective learning algorithmshould therefore have a good understanding of what information must beused for training, and what information can be overlooked. A patternthat has a negligible effect on the output may be considered asuninformative for learning purposes, while informative patterns have astrong influence on the output. The machine learning model 210 mayconstruct optimal decision boundaries over input space. Patterns closestto decision boundaries are the most informative. Selecting the mostinformative patterns, therefore, results in training only on patternsclose to boundaries.

At 314, the system 102 may be configured to determine the one or moretransport modes as the output data. The machine learning model 210 maydetermine the most likely transport mode which has been used by a userof the UE 104 in a previous window. The machine learning model 210 mayalso output the probability of each transport mode. Further, the system102 may be configured to reject a pattern whenever the classificationmay not be achieved with enough confidence. Rejection means not takingany decision. This is useful because in certain classification cases,cost of rejection may be much lower than the misclassification cost. Theclassification with reject option may comprise to train the machinelearning model 210 that rejects the one or more transport modes whenconfidence in its prediction is low so as to improve the accuracy of thenon-rejected examples and reliability of determination (or prediction)of one or more transport modes in the geographic region.

FIG. 4 illustrates a flowchart 400 for implementation of an exemplarymethod for determining one or more transport modes for one or morebuildings in a geographic region, in accordance with an embodiment. FIG.4 is explained in conjunction with FIG. 1 to FIG. 3. The control startsat 402.

At 402, sensor data associated with the one or more buildings may beobtained for time duration. The processor 202 may be configured toobtain sensor data associated with the one or more buildings for timeduration. The sensor data may be obtained from one or more userequipment (UE). In accordance with an embodiment, the UE may correspondto a mobile phone or an electronic device associated with the user.

At 404, mobility features associated with the one or more buildings maybe determined. The processor 202 may be configured to determine mobilityfeatures associated with the one or more buildings, based on the sensordata. The mobility features may correspond to one or more attributes ofthe one or more transport modes in the one or more buildings. The one ormore attributes may comprise one or more of vertical mobility signalsand transport mode usage at predefined epoch.

At 406, confidence scores for each of the mobility features may becalculated. The processor 202 may be further configured to calculateconfidence scores for each of the mobility features. The rank for eachmobility feature may indicate a measure of relevance/importance ofinformation imparted by the respective mobility feature in determinationof the one or more transport modes. The confidence scores may correspondto a rank of each of the mobility features. An order of the rank startswith a top ranked mobility feature that corresponds to a mostinformative mobility feature.

At 408, mobility feature vector of the mobility features may bedetermined. The processor 202 may be further configured to determinemobility feature vector of the mobility features based on the trainedmachine learning model 210. The mobility feature vector may correspondto a numerical representation of the one or more transport modes.

At 410, the one or more transport modes may be classified using thetrained machine learning model 210. The processor 202 may be furtherconfigured to classify the one or more transport modes using the trainedmachine learning model 210, based on the mobility feature vector.

At 412, using a trained machine learning model 210, the one or moretransport modes for the one or more buildings may be determined. Theprocessor 202 may be configured to determine, using the trained machinelearning model, the one or more transport modes for the one or morebuildings, based on the one or more mobility features. The one or moretransport modes may comprise at least one of a stair, an elevator, anescalator or none. The machine learning model 210 may correspond to aselective-learning machine learning model.

At 414, the one or more transport modes may be rejected using thetrained machine learning model 210. The processor 202 may be configuredto reject the one or more transport modes using the trained machinelearning model for determination of the one or more transport modes,based on a difference of subsequent confidence scores of the mobilityfeatures being less than a threshold value. In accordance with anembodiment, rejection may be performed by a rejection function, usingthe trained machine learning model (or rejection model), that is beinglearned from training data. An example of the rejection model is athreshold-based model that is based on geometrical distance fromtraining data centroids.

At 416, map data associated with the one or more buildings may beupdated. The processor 202 may be configured to update the map dataassociated with the one or more buildings based on the determined one ormore transport modes.

At 418, the updated map data may be transmitted to at least one subject.The processor 202 may be configured to transmit the updated map data toat least one subject. The subject may comprise at least one of a vehicleor user equipment. The control passes to the end.

Accordingly, blocks of the flowchart 400 support combinations of meansfor performing the specified functions and combinations of operationsfor performing the specified functions for performing the specifiedfunctions. It will also be understood that one or more blocks of theflowchart 400, and combinations of blocks in the flowchart 400, can beimplemented by special purpose hardware-based computer systems whichperform the specified functions, or combinations of special purposehardware and computer instructions.

Alternatively, the system may comprise means for performing each of theoperations described above. In this regard, according to an exampleembodiment, examples of means for performing operations may comprise,for example, the processor 202 and/or a device or circuit for executinginstructions or executing an algorithm for processing information asdescribed above.

On implementing the method 400 disclosed herein, the end resultgenerated by the system 102 is a tangible determination of one or moretransport modes. The determination of one or more transport modes is ofutmost importance as only highly accurate output data from thedetermined one or more transport modes may be further used in thetransport modes may be used for, but not limited to, planning commutemodes for a building, raising an alarm in case of over utilization of aparticular transport mode over other transport modes, planningmulti-modal transport routes for users (nowadays, many buildings haveself-balancing scooters or hoverboards and a user coming to a mall maybe informed if such a mode of commute is available for a building),differently abled people (such as those using wheel chair) may beinformed if there is a wheelchair accessible area in the building or canthey take their wheelchairs inside (for example, if there are onlyescalators and stairs in a building, they may not be able to usewheelchairs).

Although the aforesaid description of FIGS. 1-4 is provided withreference to the sensor data, however, it may be understood that theinvention would work in a similar manner for different types and sets ofdata as well. The system 102 may generate/train the machine learningmodel 210 to evaluate different sets of data at various geographiclocations. Additionally, or optionally, the determined one or moretransport modes in the form of output data may be provided to an enduser, as an update which may be downloaded from the mapping platform110. The update may be provided as a run time update or a pushed update.

FIG. 5 illustrates a schematic diagram 500 in an exemplary scenario forusing a system for determining a building type of one or more buildingsin a geographic region, in accordance with an embodiment. FIG. 5 isexplained in conjunction with FIG. 1 to FIG. 4. Determination of thebuilding type of the one or more buildings corresponds to classificationof the one or more buildings into one or more types of buildings. Thereis shown a dataset 502 associated with different types of buildings,feature extraction, feature vectorization and local embedding 504performed by the system for determining one or more buildings in ageographic region, learning 506 associated with a machine learningmodel, classification of buildings into different types 508, 510, 512,prediction 514 associated with trained machine learning model whichoutputs a classified building when new building data is fed to thetrained machine learning model.

In accordance with an embodiment, a user equipment such as a mobilephone may be configured with multiple sensors, such as a gyroscopicsensor and an accelerometer. The sensor data may comprise a fixed set ofattributes and corresponding values obtained from UE, (such as themobile phone). In accordance with an embodiment, initially data labelingof the sensor data may be done by a user of the mobile phone where theuser may annotate the one or more transport modes that are used by theuser during a trip of the user in the one or more buildings and thesensor data. Further, the system may be configured to obtain labeledsensor data to determine mobility features. In accordance with anembodiment, at discrete time points, the system may be configured toobtain the sensor data from the mobile phone. For another example, fromthe accelerometer sensor, the correlations in accelerations in the x, y,and z planes are used as mobility features. With these classificationfeatures (or mobility features), a machine learning model may be trainedwith the labeled sensor data.

The system for determining the building type of one or more buildingsmay be configured to obtain the mobility features associated with theone or more buildings. The mobility features may be obtained from sensordata of one or more UE. In an example embodiment, the sensor data may becollected automatically by a tracked mobile device (UE) (e.g. a personwith a mobile phone or a robot) traversing through a building. Usingplatform of the mobile device for determining building type of the oneor more buildings makes the system convenient because a user has no needto carry additional hardware. In accordance with an embodiment, thesystem may be communicatively coupled to the UE (or the mobile device).In accordance with another embodiment, the system may be a part of theUE.

In accordance with an embodiment, the mobility features may be obtainedfrom sensor data of the one or more buildings for a fixed epoch. Themobility features may correspond to one or more attributes of the one ormore buildings. The one or more attributes may comprise one or more of anumber of floors, a usability of the one or more transport modes, aspeed of the one or more transport modes, a number of the one or moretransport modes, or a volume of the one or more transport modes.

The system may be configured to extract mobility features. The systemmay be configured to extract the mobility features so that travelingpatterns associated with the one or more transport modes may bedetermined in sensor traces from the sensor data obtained from one ormore UE (such as, the UE 104) based on which classification of buildingsmay be done. Different mobility features may be used to detect a sameclass (such as, but not limited to, a residential building, a commercialbuilding, a single-storey building or multi-storey building) accurately.The feature extraction is the step needed to extract properties of thesensor data which may help in discrimination of different transportmodes from the one or more transport modes and based on which class ofbuildings may be determined.

In an example embodiment, features that are extracted from the sensordata may be associated with an elevator (one of the transport modes).Such features may be associated with vertical direction as there is nohorizontal movement of the elevator. Similarly, features of an escalatormay be associated with both horizontal and vertical direction.

The extracted features from the sensor data may be used togenerate/train a machine learning model as one or more tracked UE (suchas, the mobile phone) traverse a building. The system may be configuredto determine, using the trained machine learning model, one or moretransport modes for the one or more buildings, based on the mobilityfeatures. The one or more transport modes may include, but not limitedto, a stair, an elevator, and an escalator.

The machine learning model may be trained on the training data 502(sensor data) associated with different types of buildings, such ashospital 502A, first office building 502B, an educational institute502C, a first residential building 502D, a mall 502E, a secondresidential building 502F, a metro station 502G, a second officebuilding 502H and a third residential building 502I. The different typesof building may include, but not limited to, a mall, a residential area,an educational institute, an office building, a hospital, and a metrostation.

At 504, feature extraction, feature vectorization and local embeddingmay be performed by the system using a machine learning model. Forinference, to determine the one or more transport modes, the samemobility features utilized in training the machine learning model may beextracted. Subsequently, given these mobility features, the system withalready trained machine learning model may be configured to predict theone or more transport modes of a user probabilistically and thendetermine the one or more buildings into different types of buildings.

Further, the system may be configured to perform feature vectorizationon extracted mobility features for a feature vector using the machinelearning model. The system may be further configured to perform localembedding on the feature vectors, using the machine learning model.Feature extraction, feature vectorization and local embedding have beenexplained in detail in description of FIG. 3.

The machine learning model may be in learning phase 506 during thefeature extraction, the feature vectorization and the local embedding.The mobility features that may be useful for classification of the oneor more transport modes may be created for training the machine learningmodel during training or learning phase. The machine learning model mayinclude base-level models, such as, but not limited to, Decision Treesand more advanced complex meta-level classification models, such asVoting and Stacking.

The system may be further configured to determine, using the trainedmachine learning model, building type of the one or more buildings,based on the determined one or more transport modes. Determination ofthe building type of the one or more buildings corresponds toclassification of the one or more buildings into one or more types ofbuildings. The one or more types of the buildings may comprise a mall, aresidential building, an office building, an educational building, and ahospital. For example, in an office building, the frequency to useelevator is higher during morning and evening (i.e. office enteringhours and office leaving hours) as compared to the other timings of theday. Similarly, in a residential building, the frequency to use stairsand elevators is equally high, but in most of the residential building,there may be no escalators. Further, in an example embodiment, themobility features associated with a mall entrance 502 e may be based onrecognition that the number of floors is less as compared to residentialbuildings. Furthermore, in an example embodiment, the mobility featuresmay be associated with the usage of escalator and elevator. For example,on weekends, the usage of escalator and elevator is more in mall.Similarly, in an educational institute, users who use stairs are moreand availability of elevators and escalators is very rare. Further, thenumber of floors in a residential building 502I is more as compared to amall or an educational institute.

Based on the learning 506 by the machine learning model, the system maybe configured to classify the mobility features (or the feature vectorof the mobility features) using the machine learning model. Further, thesystem may be configured to classify the training data 502 intodifferent classes of buildings 508, 510, and 512 based on the mobilityfeatures.

At 508, the office buildings such as 502H and 502B may classified in afirst category. At 510, the buildings such as a mall 502E, hospital502A, metro station 502G, an educational institute 502C may beclassified in a second category. At step 512, the residential buildingssuch as 502D, 502I and 502F may be considered in a third category. Themachine learning model may be trained on the mobility features toclassify different types/categories of buildings.

At 514, the new sensor data may be obtained associated with buildingdata by the system to predict the classification of a new building. Thesystem may be configured to extract new mobility features correspondingto the new sensor data for the determination of the one or morebuildings. The system may be configured to determine, using the trainedmachine learning model, the one or more buildings (classifiedbuildings), based on the determined one or more transport modes.

The system may be configured to update map data associated with the oneor more buildings based on the classification of the one or morebuildings. In accordance with an embodiment, the system may beconfigured to transmit the updated map data to at least one subject. Thesubject may comprise at least one of a vehicle or user equipment.

In an example embodiment, a user moving in vehicle from a sourcelocation to a destination location may wish to know about a particularbuilding type at a certain geographic region. In such scenarios, thesystem may predict/determine the type of building using machine learningmodel based on the trained data in real time.

In an example embodiment, if a user wants to know direction and locationof a particular restaurant in a building, the system may provide exactlocation of the restaurant, such as number of floor on which therestaurant is located. Conventional navigation system may provideinformation about the distance of the restaurant from current location.However, such navigational system may not provide information about thefloor on which the restaurant is present, and that information ismanually labelled. The system disclosed herein may provide the exactlocation and floor number of the restaurant.

FIG. 6 illustrates a schematic diagram 600 in an exemplary scenario forusing a system for determining population distribution of usersassociated with one or more buildings in a geographic region, inaccordance with an embodiment. FIG. 6 is explained in conjunction withFIG. 1 to FIG. 5. There is shown a population distribution system forservices 602, and a plurality of services, viz., streetlight volume andscheduling 604, trash pickup time 606, street congestion 608, design andplanning of infrastructure 610, city level planning 612 and public spaceplanning 614.

The system for determining population distribution of users associatedwith one or more buildings in a geographic region (hereinafter referredas population distribution system 602) may comprise at least onenon-transitory memory configured to store computer executableinstructions and at least one processor configured to execute thecomputer executable instructions.

The population distribution system 602 may be configured to obtain aplurality of mobility features (hereinafter referred as mobilityfeatures) associated with the one or more buildings in geographicregion. The population distribution system 602 may be configured todetermine, using a trained machine learning model, one or more transportmodes for one or more buildings. The one or more transport modes mayinclude, but not limited to, stairs, elevators and escalators.

The population distribution system 602 may be configured to determine,using the trained machine learning model, the population distribution ofthe users associated with the one or more buildings in the geographicregion at a fixed epoch based on the determined one or more transportmodes. The population distribution system 602 may deal with populationinto/out of a building, block or neighborhood. A daily distribution ofpopulation may be modeled using a trained machine learning model in thepopulation distribution system 602 to optimize corresponding cityservices or services in a certain geographic region (such asgeo-location service).

In an example embodiment, if the population into/out of a building ismore during office hours, then the building may be classified as anoffice building by the population distribution system 602. Further, thepopulation distribution system 602 may be configured to control anapplication of streetlight volume and scheduling 604 based on thepopulation distribution in an area of street lighting. The populationdistribution in an area of street lighting may determine number ofstreetlights in that area, timings to switch on/off the streetlights.Similarly, if the building is classified as a residential building, thenthe area corresponds to residential area and consequently, thestreetlight volume 604 is higher and better. The number of streetlightsshould be higher along all public spaces, especially in areas such asintersections, pedestrian crossings, sidewalks, transit facilities suchas bus stops, and narrow streets.

In an example embodiment, services provided in a city by differentorganizations of the city may be determined by the populationdistribution system 602 based on the population into/out of a building.For example, an area considered as a residential area may populationinto/out of a building as compared to other areas and accordingly, thetrash pick-up services 606 may be scheduled suitably and provided to theresidents of the area. Similarly, if the population into/out of abuilding is very less than the trash pick-up service 606 may bescheduled suitably, for example, weekly or twice a week.

In an example embodiment, the street congestion 608 and parkingcongestion is high during office hours near office buildings/areas aspopulation into/out of an official building during office hours is more.The population distribution system 602 may be used to direct usersduring such office hours to follow routes that are less congested bynavigation applications.

In an example embodiment, the population distribution system 602 may beconfigured to utilize the data associated with population into/out of abuilding, a block or neighborhood to create resources, such as maps andschedules to direct future design projects for design and planning ofinfrastructure 610, city level planning 612 and public space planning614.

In an example embodiment, for design and planning of infrastructure 610of a city, the data associated with population into/out of a building,block, and neighborhood may be used by the population distributionsystem 602 to control applications to design and plan areas suitable formetro construction. The areas suitable for metro construction may bedetermined based on the capacity of population to travel in someparticular area. If population going to some particular areas is less,then public transport such as bus services may be provided.

In an example embodiment, for city level planning 612, the populationdistribution system 602 may use data associated with population into/outof a building, block, and neighborhood to control applications forinstalling devices for surveillance and monitoring of streets to improvesafety and automobile vigilance. Security cameras may be installed inpublic spaces to monitor crimes and other unwanted activities. Thepopulation distribution system 602 may be configured to controlapplications for public space planning 614, such as parks, playgroundsfor kids near residential areas and other services such as smallmarkets.

The population distribution system 602 may be configured to update mapdata for the one or more buildings, based on the determined populationdistribution of users associated with the one or more buildings.Therefore, the data or information provided by the populationdistribution system may be updated on map database that may be retrievedlater by organizations for optimizing the city services such as toimprove public space planning, for city level planning and designing andinfrastructure of the city.

Therefore, the mobility features associated with determined populationdistribution into/out of a building, block, and neighborhood mayfacilitate data service innovations by controlling an array ofapplications on the one or more UE, such as mobile phones. Mobileservice applications on the mobile phones may be controlled by thepopulation distribution system 602, based on mobility features relatedto individuals as well as communities. Therefore, embodiments of thepopulation distribution system 602 may provide user experiences based onthe mobility patterns extracted from the sensors on the UE of the users.The population distribution system 602 may facilitate complex dynamicinteractions among components of the population distribution system 602.The components of the population distribution system 602 may include,but not limited to, population, resources (such as shelter, and space),and environment.

FIGS. 7A and 7B collectively illustrate schematic diagrams 700A and 700Bin an exemplary scenario for using a system for determining user profileof one or more users in one or more buildings in a geographic region, inaccordance with an embodiment. FIGS. 7A and 7B is explained inconjunction with FIG. 1 to FIG. 6.

With reference to FIG. 7A, there is shown a system 702, user equipment(UE) 704, a server 706, a machine learning model 708, sensor data 710,user profiling data 712 and different user profiles 714, 716 and 718.The system 702 may comprise at least one non-transitory memoryconfigured to store computer executable instructions and at least oneprocessor configured to execute the computer executable instructions.The system 702 may be communicatively coupled to the UE 704 and theserver 706, via a network (not shown in the FIG. 7A). In accordance withan embodiment, the system 702 may be embodied in the UE 704.

In accordance with an embodiment, the UE 704 may transmit the sensordata 710 immediately to the system 102. In accordance with an alternateembodiment, the UE 704 may transmit the sensor data 710 to the system102 in batches from the server 706. In such a case, the system 702 mayaccess the one or more UE (such as, the UE 704) periodically forobtaining the sensor data, via the server 706.

The system 702 may be configured to obtain mobility features associatedwith the one or more buildings in a geographic region. The system 702may be configured to determine, using a trained machine learning model(such as the machine learning model 708), one or more transport modesfor the one or more buildings, based on the mobility features. The oneor more transport modes for the one or more buildings may include, butnot limited to, stairs, elevators and escalators.

The determination of the one or more transport modes is important foractivity recognition associated with various user profiles. For example,a user that utilizes a staircase may be more health aware user ascompared to the one using the elevator. A user that is using the stairsshould burn more calories than riding the elevator for a same trip.

The system 702 may be configured to obtain indoor mobility data of theone or more users, based on the one or more transport modes for the oneor more buildings. The indoor mobility data corresponds to informationindicating user-preferred commuting ways within a building. The indoormobility data may include at least one of a mode of commute used by theuser, or a number of times the user used the mode of transport within adefined time period. For example, when visiting a mall with a stroller,a user hardly uses the stairs (unlike when visiting without a strollerone can use the stairs and the escalators). Health aware users might usestairs more often. Further new visitors and recurrent visitors mightchange their mobility patterns. As an example, if a user always takesthe elevators in other buildings and starts by taking the stairs at abuilding the user visits. If the former behavior is due to the userbeing a “new” visitor, over time the user may start taking the elevatorinstead and returning to “natural” indoor mobility pattern. Suchdifferent behaviors of the users may be extracted by the system 702 withthe help of standard user-profiling tools. The mode of commute usedoften by the user may be determined as preferred mode of commute withinbuilding for the user. The above mentioned commuting behaviors of usersmay be captured and stored in the system 702 as indoor mobility data,and later the indoor mobility data may be used by the system 702 todetermine the user profile of the one or more users in the one or morebuildings.

In accordance with an embodiment, the processor of the system 702 maytrain the machine learning model 708 to determine the one or moretransport modes for one or more buildings in a geographic region. Inaccordance with an embodiment, the machine learning model 708 may betrained offline to obtain a classifier model to determine the one ormore transport modes for one or more buildings in a geographic region asa function of mobility features. For the training of the machinelearning model 708, different feature selection techniques andclassification techniques may be used. Examples of the machine learningmodel 708 may include, but not limited to, Decision Tree (DT), RandomForest, and Ada Boost. In accordance with an embodiment, the memory mayinclude processing instructions for training of the machine learningmodel 708 with data set that may be real-time (or near real time) dataor historical data. In accordance with an embodiment, the data may beobtained from one or more service providers.

The processor of the system 702 may be further configured to obtain thetrained machine learning model 708 and determine mobility features fromthe sensor data obtained from the one or more UE, such as the UE 704 fordetermination of the user profile of the one or more users in the one ormore buildings based on the indoor mobility data. Therefore, theprocessor of the system 702 may be configured to determine, using thetrained machine learning model 708, the user profile of the one or moreusers in the one or more buildings based on the indoor mobility data.The user profile of the one or more users may comprise a health awareuser, a user with a stroller, a recurrent visitor and a new visitor.

With reference to FIG. 7B, there is shown different user profiles, viz.,first user profile 714, a second user profile 716 and a third userprofile 718. The first user profile 714 may correspond to a health awareuser that uses stairs 720 more often to burn calories and stay fit. Ifpeople are using stairs 720 very often and uses elevators rarely thenpeople might be considered as health aware users. In such cases, peoplemay use stairs in offices, or in apartments or while going to a fitnesscenter.

The second user profile may correspond to elderly users who use anescalator 722 (two views of the escalators are shown in FIG. 7B) and anelevator 724. For example, if an old age person is present in a mall,then there might be a possibility that the old age person may use eitherthe elevator 724 or the escalator 722.

The third user profile 718 may correspond to a user with a stroller whomay use an elevator 726. In accordance with an embodiment, peoplecarrying a shopping trolley with them must prefer elevator 726 insteadof stairs and escalators. Such people may be seen at shopping malls. Inanother example, if a lady is carrying a baby in a stroller, then thelady may use elevators for obvious reasons. In such cases, the lady maybe present in shopping center, hospital or hotel.

Therefore, the indoor mobility data associated with the system 702 mayfacilitate data service innovations by controlling an array ofapplications on the one or more UE, such as mobile phones. The system702 may facilitate personalized services to the one or more users, basedon the determined user profile of the one or more users in the one ormore buildings which may further result in user satisfaction. Thepersonalized services based on the determined user profiles andcontextual information may help present relevant and accurateinformation to the one or more users.

FIGS. 8A and 8B, collectively illustrate schematic diagrams 800A and800B in an exemplary scenario for using a system for determiningmobility pattern of one or more users for one or more buildings in ageographic region, in accordance with an embodiment.

With reference to FIG. 8A, there is shown a mobility prediction system802, a machine learning model 804, one or more user data, viz., user 1sensor data 806, user 2 sensor data 808, and user 3 sensor data 810, andoutput data, viz. mobility prediction data 812 of user 1 to user N.

The mobility prediction system 802 may comprise at least onenon-transitory memory configured to store computer executableinstructions and at least one processor configured to execute thecomputer executable instructions.

The mobility prediction system 802 may be configured to obtain mobilityfeatures associated with the one or more buildings in a geographicregion. The mobility features may correspond to one or more attributesof the one or more transport modes in the one or more buildings. The oneor more attributes may comprise one or more of vertical mobility signalsand transport mode usage at predefined epoch. The one or more attributesmay further comprise mean of acceleration, standard deviation ofacceleration, yx ratio of acceleration, high peak and low peak ofacceleration, correlation of acceleration, and Signal Magnitude Area(SMA) of acceleration. The mobility prediction system 802 may be furtherconfigured to obtain entry-exit data of the one or more users for theone or more buildings in the geographic region. The entry-exit data andmobile features may be obtained from the one or more user data, viz.,user 1 sensor data 806, user 2 sensor data 808, and user 3 sensor data810.

The mobility prediction system 802 may be further configured todetermine, using a trained machine learning model, one or more transportmodes for the one or more buildings, based on the mobility features. Inaccordance with an embodiment, the mobility prediction system 802 may befurther configured to determine, using a trained machine learning model,one or more transport modes for the one or more buildings,probabilistically instantaneously. For parking spots detection 820 (asdescribed in FIG. 8B) by transitions of the one or more transport modes,the mobility prediction system 802 may forward the determined transportmode to the machine learning model 804 for further processing.

The mobility prediction system 802 may be further configured todetermine, using the trained machine learning model, mobility pattern ofthe one or more users (the mobility prediction data of user 1 to user N)based on the entry-exit data of the one or more users and the one ormore transport modes for the one or more buildings. The mobilitypatterns may correspond to travelling patterns of the one or more users(such as, user 1, user 2 and user 3) based on usage of the one or moretransport modes by the users.

The mobility prediction system 802 may be further configured to controlone or more mobility service applications on a user equipment based onthe determined mobility pattern. The one or more mobility serviceapplications may be associated with mobility services in the geographicregion. In accordance with an embodiment, the mobility prediction system802 may be further configured to control the one or more mobilityservice applications on the user equipment in near real time based onthe determined mobility pattern. The mobility service applications mayprovide services comprising at least one of traffic congestions,expected parking spots, micro-mobility dispatch, scheduling for packagedeliveries, expected mobility-as-a-service demand or expectedmobility-as-a-service supply.

The mobility prediction system 802 may be configured to use machinelearning strategies for identifying current or past activities of one ormore users, given mobility patterns of the one or more users. Forexample, the mobility patterns of the one or more users obtained fromthe sensors of UE (such as smartphones) may be utilized as trainingexamples for the machine learning model 804. The machine learning model804 may further identify the activity of the one or more usersprobabilistically. The machine learning model 804 may determine sequenceof changes in the determined one or more transport modes of the one ormore users.

With reference to FIG. 8B, there is shown mobility prediction services814 which are provided by the mobility prediction system 802, aplurality of services, namely, pick up time for mobility service 816,traffic congestion 818, expected parking spots 820, and schedule timefor package deliveries 822.

For expected parking spots 820, the mobility prediction system 802 mayconsider real-time parking observations obtained from sparse parkingstatus detectors aggregated with historic information (i.e. mean andvariance) about parking availability on a given street block. Thehistoric information is referred to as historic availability profile andis derived from sparse and error prone parking status detectors.

For the transition scheme, the one or more transport modes determined bythe mobility prediction system 802 from the sensor data of sensors (suchas, a gyroscopic sensor, and an accelerometer) are first used as inputto the machine learning model 804 (such as, a parking finite statemachine). Given these activity transition inputs, the parking finitestate machine can detect when and where the one or more users parkedtheir vehicles and which parking spots are available.

Availability of vacant parking spaces may be calculated by externalsensors such as those installed in the road surface. However, suchexternal sensors may be expensive to deploy and maintain. The mobilityprediction system 802 may be configured to obtain sensor data fromsensors on mobile phones and not external sensors. External sensors mayunderperform in extreme weather. For example, in heavy snow theseexternal road surface implanted sensors may be covered. Using mobilephones is cheaper, more convenient, and more flexible. The mobilityprediction system 802 may use sensors available on mobile phones toinfer pick up time for any mobility service 816, traffic congestion 818,expected parking spots 820, and schedule time for package deliveries822.

Therefore, the mobility prediction system 802 may facilitate dataservice innovations by controlling an array of applications (findingparking spot) on the one or more UE, such as mobile phones by usingsensors installed on the one or more UE. Such utilization of the sensorsby the mobility prediction system 802 is cheap, convenient and flexibleas compared to external sensors installed in external environment.

FIG. 9 illustrates a flowchart for implementation of an exemplary methodfor determining one or more types of buildings in a geographic region,in accordance with an example embodiment. FIG. 9 is explained inconjunction with FIG. 1 to FIG. 8B. The control starts at 902.

In accordance with an embodiment, a system for determining building typeof one or more buildings in a geographic region may comprise means forperforming each of the operations described below. In this regard,according to an example embodiment, examples of means for performingoperations may comprise, for example, a processor and/or a device orcircuit for executing instructions or executing an algorithm forprocessing information as described below.

At 902, mobility features associated with the one or more buildings maybe obtained. In accordance with an embodiment, the processor may beconfigured to obtain mobility features associated with the one or morebuildings. The mobility features may be obtained from sensor data of theone or more buildings for fixed epoch. The mobility features maycorrespond to one or more attributes of the one or more buildings. Theone or more attributes may comprise one or more of a number of floors,usability of the one or more transport modes, speed of the one or moretransport modes, a number of the one or more transport modes, or volumeof the one or more transport modes.

At 904, using a trained machine learning model, one or more transportmodes for the one or more buildings may be determined. The processor maybe configured to determine, using a trained machine learning model, oneor more transport modes for the one or more buildings, based on themobility features. The one or more transport modes comprise at least oneof a stair, an elevator, an escalator, a travellator or the like.

At 906, using the trained machine learning model, the building type ofone or more buildings may be determined. The processor may be configuredto determine, using the trained machine learning model, building type ofthe one or more buildings, based on the determined one or more transportmodes. Determination of the building type of one or more buildings maycorrespond to classification of the one or more buildings into one ormore types of buildings. The one or more types of the buildings maycomprise at least one of a mall, a residential building, an officebuilding, an educational building, or a hospital.

At 908, map data associated with the one or more buildings may beupdated. The processor may be configured to update map data associatedwith the one or more buildings based on the classification of the one ormore buildings.

At 910 the updated map data to at least one subject may be transmitted.The processor may be configured to transmit the updated map data to atleast one subject. The subject may comprise at least one of a vehicle oruser equipment.

At 912, input specifying information related to a first building in ageographic region may be received. The processor may be configured toreceive input specifying information related to the first building in ageographic region. The input may be received from a UE of a user. Inaccordance with an embodiment, the input may correspond to a request fordetermination of a building type of the first building in the geographicregion. Further, the processor may perform a search for sensor datawithin the geographic region. Further, the processor may obtain map dataassociated with the first building in the geographic region.

At 914, a notification message indicating the determination of buildingtype of the first building may be generated. The processor may befurther configured to generate a notification message indicating thedetermination of the building type of the first building. The controlpasses to the end.

Accordingly, blocks of the flowchart 900 support combinations of meansfor performing the specified functions and combinations of operationsfor performing the specified functions for performing the specifiedfunctions. It will also be understood that one or more blocks of theflowchart 900, and combinations of blocks in the flowchart 900, can beimplemented by special purpose hardware-based computer systems whichperform the specified functions, or combinations of special purposehardware and computer instructions.

FIG. 10 illustrates a flowchart for implementation of an exemplarymethod for determining population distribution of users for one or morebuildings in a geographic region, in accordance with an exampleembodiment. FIG. 10 is explained in conjunction with FIG. 1 to FIG. 9.The control starts at 1002.

In accordance with an embodiment, a system for determining populationdistribution of users associated with one or more buildings in ageographic region may comprise means for performing each of theoperations described below. In this regard, according to an exampleembodiment, examples of means for performing operations may comprise,for example, a processor and/or a device or circuit for executinginstructions or executing an algorithm for processing information asdescribed below.

At 1002, mobility features associated with one or more buildings in ageographic region may be obtained. The processor may be configured toobtain mobility features associated with the one or more buildings inthe geographic region.

At 1004, one or more transport modes for the one or more buildings maybe determined. The processor may be further configured to determine,using a trained machine learning model, the one or more transport modesfor the one or more buildings.

At 1006, population distribution of the users associated with the one ormore buildings in the geographic region may be determined. The processormay be configured to determine, using trained machine learning model,the population distribution of the users for the one or more buildingsin the geographic region at a fixed epoch based on the determined one ormore transport modes.

At 1008, map data for the one or more buildings may be updated. Theprocessor may be configured to update the map data for the one or morebuildings, based on the determined population distribution of usersassociated with the one or more buildings.

At 1010, the updated map data may be transmitted to at least onesubject. The processor may be configured to transmit the updated mapdata to at least one subject, wherein the subject comprises at least oneof a vehicle or user equipment.

At 1012, one or more geo-location service applications may be controlledon user equipment. The processor may be configured to control one ormore geo-location service applications on user equipment based on thedetermined population distribution of the users associated with the oneor more buildings in the geographic region. The one or more geo-locationservice applications may be associated with geo-location services in thegeographic region. The geo-location services may comprise, but notlimited to, streetlight volume, scheduling, trash pickup time, streetcongestion, traffic planning, parking planning, transportation planning,public space planning, and city level planning. The control passes tothe end.

Accordingly, blocks of the flowchart 1000 support combinations of meansfor performing the specified functions and combinations of operationsfor performing the specified functions for performing the specifiedfunctions. It will also be understood that one or more blocks of theflowchart 1000, and combinations of blocks in the flowchart 1000, can beimplemented by special purpose hardware-based computer systems whichperform the specified functions, or combinations of special purposehardware and computer instructions.

FIG. 11 illustrates a flowchart for implementation of an exemplarymethod for determining a user profile of one or more users in one ormore buildings in a geographic region, in accordance with an embodiment.FIG. 11 is explained in conjunction with FIG. 1 to FIG. 10. The controlstarts at 1102.

In accordance with an embodiment, a system for determining a userprofile of one or more users in one or more buildings in a geographicregion may comprise means for performing each of the operationsdescribed below. In this regard, according to an example embodiment,examples of means for performing operations may comprise, for example, aprocessor and/or a device or circuit for executing instructions orexecuting an algorithm for processing information as described below.

At 1102, mobility features associated with the one or more buildings ina geographic region may be obtained. The processor may be configured toobtain the mobility features associated with the one or more buildingsin the geographic region. The mobility features may comprise at leastone of one or more vertical mobility signals, or one or more transportmode usage.

At 1104, one or more transport modes may be determined for the one ormore buildings. The processor may be configured to determine, using atrained machine learning model, the one or more transport modes for theone or more buildings, based on the mobility features.

At 1106, indoor mobility data of the one or more users may be obtained.The processor may be configured to obtain indoor mobility data of theone or more users based on the one or more transport modes for the oneor more buildings.

At 1108, a user profile of the one or more users may be determined inthe one or more buildings. The processor may be configured to determine,using the trained machine learning model, the user profile of the one ormore users in the one or more buildings based on the indoor mobilitydata. The user profile of the one or more users may comprise a healthaware user, a user with a stroller, a recurrent visitor and a newvisitor. The control passes to the end.

Accordingly, blocks of the flowchart 1100 support combinations of meansfor performing the specified functions and combinations of operationsfor performing the specified functions for performing the specifiedfunctions. It will also be understood that one or more blocks of theflowchart 1100, and combinations of blocks in the flowchart 1100, can beimplemented by special purpose hardware-based computer systems whichperform the specified functions, or combinations of special purposehardware and computer instructions.

FIG. 12 illustrates a flowchart for implementation of an exemplarymethod for determining mobility pattern of one or more users for one ormore buildings in a geographic region, in accordance with an embodiment.FIG. 12 is explained in conjunction with FIG. 1 to FIG. 3. The controlstarts at 1202.

In accordance with an embodiment, a system for determining mobilitypattern of one or more users for one or more buildings in a geographicregion may comprise means for performing each of the operationsdescribed below. In this regard, according to an example embodiment,examples of means for performing operations may comprise, for example, aprocessor and/or a device or circuit for executing instructions orexecuting an algorithm for processing information as described below.

At 1202, mobility features associated with the one or more buildings ina geographic region, entry-exit data of the one or more users for theone or more buildings in the geographic region may be obtained. Theprocessor may be configured to obtain the mobility features associatedwith the one or more buildings in the geographic region and entry-exitdata of the one or more users for the one or more buildings in thegeographic region.

At 1204, one or more transport modes may be determined for the one ormore buildings. The processor may be further configured to determine,using a trained machine learning model, the one or more transport modesfor the one or more buildings, based on the mobility features.

At 1206, mobility pattern of the one or more users may be determined.The processor may be configured to determine, using the trained machinelearning model, the mobility pattern of the one or more users based onthe entry-exit data of the one or more users and the one or moretransport modes for the one or more buildings.

At 1208, one or more mobility service applications may be controlled onuser equipment. The processor may be configured to control the one ormore mobility service applications on the user equipment based on thedetermined mobility pattern. The one or more mobility serviceapplications may be associated with mobility services in the geographicregion. In accordance with an embodiment, the processor may beconfigured to control the one or more mobility service applications onthe user equipment in near real time based on the determined mobilitypattern. The one or more mobility service applications may provideservices comprising at least one of traffic congestions, expectedparking spots, micro-mobility dispatch, scheduling for packagedeliveries, expected mobility-as-a-service demand or expectedmobility-as-a-service supply. The control passes to the end.

Accordingly, blocks of the flowchart 1200 support combinations of meansfor performing the specified functions and combinations of operationsfor performing the specified functions for performing the specifiedfunctions. It will also be understood that one or more blocks of theflowchart 1200, and combinations of blocks in the flowchart 1200, can beimplemented by special purpose hardware-based computer systems whichperform the specified functions, or combinations of special purposehardware and computer instructions.

Many modifications and other embodiments of the disclosures set forthherein will come to mind to one skilled in the art to which thesedisclosures pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the disclosures are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe example embodiments in the context of certain examplecombinations of elements and/or functions, it should be appreciated thatdifferent combinations of elements and/or functions may be provided byalternative embodiments without departing from the scope of the appendedclaims. In this regard, for example, different combinations of elementsand/or functions than those explicitly described above are alsocontemplated as may be set forth in some of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A system to determine population distribution of users associatedwith one or more buildings in a geographic region, comprising: at leastone memory configured to store computer executable instructions; and atleast one processor configured to execute the computer executableinstructions to: obtain a plurality of mobility features associated withthe one or more buildings in the geographic region, wherein theplurality of mobility features comprise one or more of vertical mobilitysignals; determine, using a trained machine learning model, one or moretransport modes for the one or more buildings, based on the plurality ofmobility features; and determine, using the trained machine learningmodel, the population distribution of the users associated with the oneor more buildings in the geographic region at a fixed epoch based on thedetermined one or more transport modes.
 2. The system of claim 1,wherein the at least one processor is further configured to update mapdata for the one or more buildings, based on the determined populationdistribution of the users associated with the one or more buildings. 3.The system of claim 2, wherein the at least one processor is furtherconfigured to transmit the updated map data to at least one subject,wherein the subject comprises at least one of a vehicle or a userequipment.
 4. The system of claim 1, wherein the at least one processoris further configured to control one or more geo-location serviceapplications on a user equipment based on the determined populationdistribution of the users associated with the one or more buildings inthe geographic region.
 5. The system of claim 4, wherein the one or moregeo-location service applications provide services comprising at leastone of streetlight volume, scheduling, trash pickup time, streetcongestion, traffic planning, parking planning, transportation planning,public space planning, or city level planning.
 6. A method to determinepopulation distribution of users associated with one or more buildingsin a geographic region, comprising: obtaining a plurality of mobilityfeatures associated with the one or more buildings in the geographicregion, wherein the plurality of mobility features comprise one or moreof vertical mobility signals; determining, using a trained machinelearning model, one or more transport modes for the one or morebuildings, based on the plurality of mobility features; and determining,using the trained machine learning model, the population distribution ofthe users associated with the one or more buildings in the geographicregion at a fixed epoch based on the determined one or more transportmodes.
 7. The method of claim 6, wherein the at least one processor isfurther configured to update the map data for the one or more buildings,based on the determined population distribution of the users associatedwith the one or more buildings.
 8. The method of claim 7, wherein the atleast one processor is further configured to transmit the updated mapdata to at least one subject, wherein the subject comprises at least oneof a vehicle or a user equipment.
 9. The method of claim 6, wherein theat least one processor is further configured to control one or moregeo-location service applications on a user equipment based on thedetermined population distribution of the users associated with the oneor more buildings in the geographic region.
 10. The method of claim 9,wherein the one or more geo-location service applications provideservices comprising at least one of streetlight volume, scheduling,trash pickup time, street congestion, traffic planning, parkingplanning, transportation planning, public space planning, and city levelplanning.
 11. A computer program product comprising a non-transitorycomputer readable medium having stored thereon computer executableinstructions, which when executed by one or more processors, cause theone or more processors to carry out operations for determiningpopulation distribution of users associated with one or more buildingsin a geographic region, the operations comprising: obtaining a pluralityof mobility features associated with the one or more buildings in thegeographic region, wherein the plurality of mobility features compriseone or more of vertical mobility signals; determining, using a trainedmachine learning model, one or more transport modes for the one or morebuildings, based on the plurality of mobility features; and determining,using the trained machine learning model, the population distribution ofthe users associated with the one or more buildings in the geographicregion at a fixed epoch based on the determined one or more transportmodes.